code: def __init__(
      self,
      in_channels: int,
      hidden_channels: int,
      out_channels: int,
      num_layers: int,
  ):
      super().__init__()
  
      self.convs = torch.nn.ModuleList()
      for _ in range(num_layers):
          mlp = Sequential(
              Linear(in_channels, hidden_channels),
              BatchNorm1d(hidden_channels),
              ReLU(),
              Linear(hidden_channels, hidden_channels),
          )
          self.convs.append(GINConv(mlp))
          in_channels = hidden_channels
  
      self.lin1 = Linear(hidden_channels, hidden_channels)
      self.lin2 = Linear(hidden_channels, out_channels)
  
file_path: pytorch_geometric/examples/cpp/save_model.py
imports: from typing import Optional
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import BatchNorm1d, Linear, ReLU, Sequential
  from torch_geometric.nn import GINConv, global_mean_pool
label_desc: Use image node_id 1 to create a new GIN object from inherited base classes: torch.nn with example: obj = GIN(in_channels, hidden_channels, out_channels, num_layers)